Software Engineer, Data Processing & Privacy - 26-00382

Remote Full-time
Additional Notes: Data Privacy and legal environments, working in Python & with Claude, handling/processing PII; Soft skills: attention to detail, reliable, good with reviews/audits.

About the role
β€’ Client is seeking a detail-oriented Software Engineer on a contract basis to build and run data processing pipelines for datasets used in our research. You'll take raw, heterogeneous inputs β€” text, code, documents, structured exports β€” and turn them into clean, well-structured, privacy-safe outputs ready for downstream use.
β€’ The work spans ingestion, format normalization, data quality, privacy handling (including PII de-identification), and the supporting tooling that makes the pipeline reliable and self-serve. You'll iterate closely with internal teams on QA findings and harden the pipeline so each new dataset is cheaper than the last.

Responsibilities
β€’ Build and extend per-source processing for new data types as they arrive
β€’ Ingest and normalize raw exports across many formats into consistent, well-structured outputs
β€’ Handle privacy requirements β€” for example, PII detection and de-identification β€” to meet our internal compliance bar
β€’ Run data quality QA: automated checks plus LLM-assisted review to flag gaps, malformed inputs, and incompleteness
β€’ Iterate on internal feedback: root-cause issues, fix, re-run, re-deliver
β€’ Build supporting tools: auditing, data exploration, monitoring, simple search over processed data
β€’ Land cleaned data with the right storage layout and access controls
β€’ Document and harden the pipeline so each new dataset is cheaper than the last

You may be a good fit if you
β€’ Have 4+ years of software engineering experience, with substantial time on data pipelines
β€’ Are a proficient user of Claude / Claude Code for day-to-day engineering and know when to verify its output

Are genuinely detail-oriented
β€’ Have high integrity and take handling real people's personal data seriously
β€’ Are comfortable with sustained, careful data work and find satisfaction in getting it right
β€’ Can work independently, ship reliably, and communicate clearly about progress and edge cases
β€’ Are proficient in Python and comfortable working across many heterogeneous, semi-structured formats (JSON, NDJSON, code, HTML/XML dumps, archives)
β€’ Strong candidates may also have experience with
β€’ PII detection and anonymization techniques
β€’ Working with large, messy, semi-structured text and code corpora
β€’ Data quality monitoring and validation
β€’ Cloud storage and access-control patterns (S3/GCS, IAM)
β€’ Building internal tools or self-serve data platforms for researchers
β€’ Information retrieval, search, or RAG systems.

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